- A
Fine-tune the model on a dataset of post-2023 papers and deploy it.
Why wrong: Fine-tuning adds knowledge but does not remove pre-existing knowledge; the model may still fall back on older data.
- B
Set the maxTokens to a low value to force the model to rely on recent context.
Why wrong: Token limits do not affect the model's internal knowledge; it can still use outdated information.
- C
Include a system prompt instructing the model to ignore data before 2023.
Why wrong: System prompts are guidelines; models may still use pre-trained knowledge inadvertently.
- D
Use Amazon Bedrock Knowledge Bases with a metadata filter to retrieve only papers published after 2023, and generate responses based on retrieved content.
Metadata filtering ensures only relevant recent documents are used, grounding the model in current data.
AIF-C01 Applications of Foundation Models Practice Question
This AIF-C01 practice question tests your understanding of applications of foundation models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A research team is using Amazon Bedrock to analyze scientific papers. They want the model to generate answers based only on papers published after 2023. Which approach should they use?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use Amazon Bedrock Knowledge Bases with a metadata filter to retrieve only papers published after 2023, and generate responses based on retrieved content.
Option D is correct because Amazon Bedrock Knowledge Bases with a metadata filter allows you to restrict retrieval to only documents that match specific metadata criteria, such as publication year. By filtering the vector search to only include papers published after 2023, the model generates responses based solely on that retrieved content, ensuring it does not rely on pre-2023 data. This approach is the only one that guarantees the model's answers are grounded exclusively in the specified time range.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune the model on a dataset of post-2023 papers and deploy it.
Why it's wrong here
Fine-tuning adds knowledge but does not remove pre-existing knowledge; the model may still fall back on older data.
- ✗
Set the maxTokens to a low value to force the model to rely on recent context.
Why it's wrong here
Token limits do not affect the model's internal knowledge; it can still use outdated information.
- ✗
Include a system prompt instructing the model to ignore data before 2023.
Why it's wrong here
System prompts are guidelines; models may still use pre-trained knowledge inadvertently.
- ✓
Use Amazon Bedrock Knowledge Bases with a metadata filter to retrieve only papers published after 2023, and generate responses based on retrieved content.
Why this is correct
Metadata filtering ensures only relevant recent documents are used, grounding the model in current data.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that a system prompt or fine-tuning can reliably restrict a model's knowledge to a specific time period, when in fact only a retrieval-based approach with metadata filtering can enforce such temporal constraints.
Detailed technical explanation
How to think about this question
Amazon Bedrock Knowledge Bases uses a retrieval-augmented generation (RAG) architecture where a vector store indexes document chunks along with metadata. During query time, a metadata filter is applied as a pre-retrieval step, ensuring only chunks with a publication_year > 2023 are returned from the vector search; the model then generates answers exclusively from those retrieved chunks, effectively bypassing any pre-2023 knowledge in its parameters. This approach is critical for compliance scenarios where temporal accuracy is mandatory, such as regulatory or scientific analysis.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Applications of Foundation Models — This question tests Applications of Foundation Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Amazon Bedrock Knowledge Bases with a metadata filter to retrieve only papers published after 2023, and generate responses based on retrieved content. — Option D is correct because Amazon Bedrock Knowledge Bases with a metadata filter allows you to restrict retrieval to only documents that match specific metadata criteria, such as publication year. By filtering the vector search to only include papers published after 2023, the model generates responses based solely on that retrieved content, ensuring it does not rely on pre-2023 data. This approach is the only one that guarantees the model's answers are grounded exclusively in the specified time range.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.
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